Dark Mode
High-Accuracy Augmented Machine Translation Dataset
Data Science and Analytics
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
Enhancing natural language processing capabilities requires innovative strategies for cross-lingual tasks. This collection offers a bilingual augmentation approach specifically designed for code conversion between English and Chinese. By providing instructions and their corresponding translations with a median sequence length of 471 characters, it enables researchers to refine machine translation accuracy and deepen the understanding of automated conversion processes between these two complex linguistic frameworks.
Columns
- instruction: This field contains the original English instructions used as the primary input for the augmentation process.
- output: This field provides the corresponding Chinese instructions generated through the advanced conversion method.
Distribution
The information is delivered in a CSV file titled
train.csv with a file size of 247.17 MB. It consists of 111,272 unique records across 2 distinct columns. The data maintains a high level of integrity with 100% validity for both fields and a perfect usability score of 10.00.Usage
This resource is ideal for training neural networks on advanced augmentation techniques to improve the precision of large-scale language translation projects. It can be integrated into artificial intelligence programmes focused on natural language processing and other code-related linguistic applications. Researchers can also utilise the pairs to explore new strategies for automatically translating English instructions into Chinese with high fidelity.
Coverage
The scope focuses on the linguistic relationship between English and Chinese through a bilingual code augmentation strategy. While the data is not bound to a specific geographic region or time period, it covers 111,272 unique instructional pairs. The focus is centred on the intersection of programming logic and natural language translation.
License
CC0: Public Domain
Who Can Use It
AI researchers can leverage these records to test new ideas for cross-lingual accuracy in machine translation. Machine learning engineers may utilise the augmentation strategy to bolster the performance of translation models. Additionally, developers working on bilingual code-related applications can use the structured instructions to improve automated conversion workflows.
Dataset Name Suggestions
- Evol Codealpaca V1: English-Chinese Code Augmentation
- Bilingual Instruction Conversion and Augmentation Archive
- Chinese-English NLP Code Translation Registry
- High-Accuracy Augmented Machine Translation Dataset
- Instructional Code Conversion and Language Strategy Repository
Attributes
Original Data Source:High-Accuracy Augmented Machine Translation Dataset
QUALITYLoading...
Free
Download Dataset in CSV Format
Recommended Datasets
Loading recommendations...